Abstract

Privacy Preserving Data Mining (PPDM) is an application field, which is becoming very relevant. Its goal is the study of new mechanisms which allow the dissemination of confidential data for data mining tasks while preserving individual private information. Additionally, due to the relevance of \(R\) language in the statistics and data mining communities, it is undoubtedly a good environment to research, develop and test privacy techniques aimed to data mining. In this chapter we outline some helpful tools in \(R\) to introduce readers to that field, so that we present several PPDM protection techniques as well as their information loss and disclosure risk evaluation process and outline some tools in \(R\) to help to introduce practitioners to this field.

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Acknowledgments

Partial support by the Spanish MICINN (projects COPRIVACY (TIN2011-27076-C03-03), N-KHRONOUS (TIN2010-15764), and ARES (CONSOLIDER INGENIO 2010 CSD2007-00004)) and by the EC (FP7/2007-2013) Data without Boundaries (grant agreement number 262608) is acknowledged. The work contributed by the first author was carried out as part of the Computer Science Ph.D. program of the Universitat Autónoma de Barcelona (UAB).